David G. Gobbi

1.2k total citations
44 papers, 569 citations indexed

About

David G. Gobbi is a scholar working on Computer Vision and Pattern Recognition, Surgery and Biomedical Engineering. According to data from OpenAlex, David G. Gobbi has authored 44 papers receiving a total of 569 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Vision and Pattern Recognition, 15 papers in Surgery and 14 papers in Biomedical Engineering. Recurrent topics in David G. Gobbi's work include Medical Image Segmentation Techniques (16 papers), Surgical Simulation and Training (14 papers) and Augmented Reality Applications (7 papers). David G. Gobbi is often cited by papers focused on Medical Image Segmentation Techniques (16 papers), Surgical Simulation and Training (14 papers) and Augmented Reality Applications (7 papers). David G. Gobbi collaborates with scholars based in Canada, United States and Brazil. David G. Gobbi's co-authors include Richard Frayne, Kevin Cleary, Roberto Lotufo, Letícia Rittner, Terry M. Peters, Roberto Souza, Oeslle Lucena, Stephen Aylward, Kevin Gary and Luis Ibáñez and has published in prestigious journals such as SHILAP Revista de lepidopterología, NeuroImage and IEEE Transactions on Medical Imaging.

In The Last Decade

David G. Gobbi

41 papers receiving 540 citations

Peers

David G. Gobbi

RNMI

CVPR

SURGE

Steven L. Hartmann United States

Felix Ritter Germany

Dana Cobzaş Canada

Daniel B. Russakoff United States

Grzegorz Soza Germany

Thomas Hartkens United Kingdom

Weixin Si China

Žiga Špiclin Slovenia

Markus Fangerau Germany

Jean‐Marc Peyrat France

Steven L. Hartmann United States

David G. Gobbi

191 ×0.8

RNMI

221 ×1.1

CVPR

113 ×0.6

81 ×0.6

SURGE

23 ×0.4

Citations per year, relative to David G. Gobbi David G. Gobbi (= 1×) peers Steven L. Hartmann

Countries citing papers authored by David G. Gobbi

Since Specialization

Citations

This map shows the geographic impact of David G. Gobbi's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David G. Gobbi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David G. Gobbi more than expected).

Fields of papers citing papers by David G. Gobbi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David G. Gobbi. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David G. Gobbi. The network helps show where David G. Gobbi may publish in the future.

Co-authorship network of co-authors of David G. Gobbi

This figure shows the co-authorship network connecting the top 25 collaborators of David G. Gobbi. A scholar is included among the top collaborators of David G. Gobbi based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with David G. Gobbi. David G. Gobbi is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Gobbi, David G., Cheryl R. McCreary, Feryal Saad, et al.. (2025). Attention Blocks Improve White Matter Hyperintensity Semantic Segmentation using U-Nets. IEEE Latin America Transactions. 23(8). 646–661.

Bell, Tiffany, Julie M. Joyce, David G. Gobbi, et al.. (2024). Quantitative Susceptibility Mapping in Adults with Persistent Postconcussion Symptoms after Mild Traumatic Brain Injury: An Exploratory Study. American Journal of Neuroradiology. 46(2). 435–442. 5 indexed citations

Gobbi, David G., Cheryl R. McCreary, Feryal Saad, et al.. (2024). Multi-stage semi-supervised learning enhances white matter hyperintensity segmentation. Frontiers in Computational Neuroscience. 18. 1487877–1487877. 1 indexed citations

Gobbi, David G., et al.. (2023). Changes in 3D radiomic texture descriptors in Alzheimer’s disease stages. 49–49. 1 indexed citations

Beaudin, Andrew E., Feryal Saad, Myrlene Gee, et al.. (2023). Brain iron content in cerebral amyloid angiopathy using quantitative susceptibility mapping. Frontiers in Neuroscience. 17. 1139988–1139988. 7 indexed citations

Gobbi, David G., et al.. (2022). Segmenting White Matter Hyperintensity in Alzheimer’s Disease using U-Net CNNs. 109–114. 2 indexed citations

Salluzzi, Marina, Cheryl R. McCreary, David G. Gobbi, M. Louis Lauzon, & Richard Frayne. (2022). Short-term repeatability and long-term reproducibility of quantitative MR imaging biomarkers in a single centre longitudinal study. NeuroImage. 260. 119488–119488. 5 indexed citations

Kiss, Zelma H. T., et al.. (2020). Tactics: an open-source platform for planning, simulating and validating stereotactic surgery. SHILAP Revista de lepidopterología. 25(1). 1–14. 4 indexed citations

Clark, Darren, Kara A. Johnson, Christopher R. Butson, et al.. (2020). Tract-based analysis of target engagement by subcallosal cingulate deep brain stimulation for treatment resistant depression. Brain stimulation. 13(4). 1094–1101. 29 indexed citations

10.

McCreary, Cheryl R., Marina Salluzzi, Linda Andersen, et al.. (2020). Calgary Normative Study: design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan. BMJ Open. 10(8). e038120–e038120. 11 indexed citations

11.

Souza, Roberto, Oeslle Lucena, David G. Gobbi, et al.. (2017). An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement. NeuroImage. 170. 482–494. 135 indexed citations

12.

Abolmaesumi, Purang, et al.. (2014). SimITK: Visual Programming of the ITK Image-Processing Library within Simulink. Journal of Digital Imaging. 27(2). 220–230. 1 indexed citations

13.

Gobbi, David G., et al.. (2010). Visual Programming of VTK Pipelines in Simulink.

14.

Tokuda, Junichi, Gregory S. Fischer, Simon DiMaio, et al.. (2009). Integrated navigation and control software system for MRI-guided robotic prostate interventions. Computerized Medical Imaging and Graphics. 34(1). 3–8. 44 indexed citations

15.

Karimaghaloo, Zahra, Gábor Fichtinger, David G. Gobbi, et al.. (2008). Intensity-based registration of prostate brachytherapy implants and ultrasound. PubMed. 1. 780–783. 3 indexed citations

16.

Tokuda, Junichi, Gregory S. Fischer, Csaba Csoma, et al.. (2008). Software Strategy for Robotic Transperineal Prostate Therapy in Closed-Bore MRI. Lecture notes in computer science. 11(Pt 2). 701–709. 15 indexed citations

17.

Enquobahrie, Daniel A., David G. Gobbi, Patrick Cheng, et al.. (2008). Designing tracking software for image-guided surgery applications: IGSTK experience. International Journal of Computer Assisted Radiology and Surgery. 3(5). 395–403. 22 indexed citations

18.

Enquobahrie, Daniel A., Patrick Cheng, Kevin Gary, et al.. (2007). The Image-Guided Surgery Toolkit IGSTK: An Open Source C++ Software Toolkit. Journal of Digital Imaging. 20(S1). 21–33. 66 indexed citations

19.

Gobbi, David G. & Terry M. Peters. (2003). Generalized 3D nonlinear transformations for medical imaging: an object-oriented implementation in VTK. Computerized Medical Imaging and Graphics. 27(4). 255–265. 23 indexed citations

20.

Dey, Damini, David G. Gobbi, Piotr J. Slomka, Kathleen Surry, & T.M. Peters. (2002). Automatic fusion of freehand endoscopic brain images to three-dimensional surfaces: creating stereoscopic panoramas. IEEE Transactions on Medical Imaging. 21(1). 23–30. 57 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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